New insight in severe acute respiratory syndrome coronavirus 2 consideration: Applied machine learning for nutrition quality, microbiome and microbial food poisoning concerns

Fatemeh Taslimi, Seyedeh Elham Hosseini Ezabadi, Maed Jabbari, Erfan Ghanbarzadeh, Mandana Ashkan, Farhad Akbari Afkhami, Amir Rigi, Mojtaba Hedayati Ch


Although almost two years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various researches in order to eliminate or reduce the problems caused by this disease. Nutrition is one of the sciences that plays a very important supportive role in this regard. It is important for patients to pay attention to the potential of different diets in preventing or accelerating the healing process. The relationship between nutrition and microbiome regulation or the occurrence of food microbial poisoning is one of the factors that can directly or indirectly play a key role in the body's resilience to COVID-19. In this article, we introduce a link between nutrition, the microbiome, and the incidence of food microbial poisoning that may have great potential in preventing, treating COVID-19, or preventing deterioration in patients. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies and lead to the creation of a conceptual model called "Balance square", which we will introduce.


COVID-19; Microbiome; Microbial toxins; Nutrition; Machine learning

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